h2o-llmstudio vs Soup
H2O's no-code GUI and framework for fine-tuning LLMs — LoRA, 8-bit, DPO and experiment tracking behind a web UI, with CLI and Docker paths for the same configs. — versus — Fine-tune and post-train LLMs from one YAML: QLoRA, DPO/ORPO/SimPO/KTO — layer streaming trains an 8B model in 3.3GB VRAM on a laptop GPU, bit-exact against a resident run.
Both chase no-pain fine-tuning: H2O behind a no-code GUI with experiment tracking, Soup behind a single YAML and a laptop-VRAM streaming engine.
| h2o-llmstudio | Soup | |
|---|---|---|
| Stars | 5.0k | 396 |
| Forks | 536 | 47 |
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Last activity | 4 days ago | 2 days ago |
| Topics | training | training |
| Curated connections | 5 | 2 |
h2o-llmstudio — the curator's take
Fine-tuning for teams where not everyone writes training loops: pick a base model, upload data, tune LoRA/quantization/DPO hyperparameters in a web UI, compare runs visually, export to the Hub. The CLI runs the same configs headless, so GUI experiments graduate to scripted jobs. NOT for RL post-training at scale (its RL is experimental — that's verl/slime territory) and not for frontier-size models; and it's opinionated toward the H2O ecosystem — if your workflow is already Hub-native code, a library fits better than a studio.
Soup — the curator's take
The layer-streaming trick is the story: the frozen base never sits in VRAM, so preference tuning that normally needs a second model copy runs on a 4GB laptop — and every release ships bit-exactness proofs plus a Zenodo paper, which is more rigor than most tuning tools at ★396. When NOT: multi-GPU serious runs and exotic architectures belong to llamafactory; streaming is still BETA and single-GPU-minded, and the project is one release cycle old.